Randomized Experiments in Psychology – Term Paper

Psychologists utilize experiments to examine how the manipulation of one factor results in a change of another element. Scientists refer to these factors as variables. Most experiments have two variables, namely, the independent and the dependent variables. An independent variable does not vary based on the other variable, but the dependent one does. Experiments are the best ways to establish cause and effect relationships between these variables. Psychologists count on randomized trials to assign treatment conditions to different participants in the group. Randomized experiments ensure an equal probability of assigning a study subject to any group. Indeed, this is paramount for determining that the independent variable is what generates the result.

Second, these trials create comparable intervention groups that are similar in all essential aspects except for the treatment condition each receives. Randomization requires creating randomization schedules, which a researcher can reproduce. Generation of a randomized schedule usually consists of obtaining random numbers and allocating them to each subject. Psychologists use random number tables or computer software, for instance, Statistical Analysis System (SAS) to formulate these figures. This paper seeks to study the application of randomization in psychology experiments to understand its importance, the different randomization methods, and find out how a researcher can make conclusions without randomization.

Importance of randomization

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Researchers in psychology experiments require randomization for some reasons. First, treatment conditions in diverse groups should not vary in any systematic way. Furthermore, if subjects differ systematically, a bias will occur in the research results. According to West and Spring (2014), bias distorts the actual effect of a study’s results, which frequently leads to invalid conclusions about whether a certain intervention works. Randomized experiments play an essential of minimizing bias and threats to internal validity, which is the extent to which the results are correct, by equalizing the conditions on all other influences except for the treatment condition. For example, depression increases with the start of puberty. If more participants in control than in the study group reached puberty during the study, then the researcher can explain why the control group finished the trial with more depression than in the treated group.

Second, randomization balances the groups concerning many known and unknown prognostic variables (Suresh, 2011) by minimizing threats to external validity. The main threats include sample characteristics, setting attributes, and effects due to testing. Sample characteristics refer to the qualities of the treatment group. For example, a researcher may derive a sample from participants with different demographic features such as age, gender, and ethnicity. On the other hand, setting attributes to refer to the environment in which a researcher performs the experiment, for instance, in a clinic by a highly trained therapist or in an academic setting by less professional research staff. Effects due to testing denote the potential of participants to respond differently because they know they are under study. Randomization maximizes efficacy and effectiveness of the tests by minimizing these threats.

Methods of randomization

Corbett, MoeByrne, Oddie, and McGuire (2015) enumerate two methods of randomization. First, is simple randomization a two-treatment comparison with predetermined sample sizes. It is a process, which is relatively easy to plan, preserves the predetermined design parameters, and readily applicable to situations with comparisons of multiple treatments. Another name for this method is the simple sequential randomization where both the control and study group samples are equal. In a two-group experiment, the process of choosing participants is analogous to the toss of a coin. In other words, each participant has an equal chance to join either group. While using a large sample size, researchers use simple randomization to generate approximately equal treatment groups.

Second, the block randomization method, also called stratified randomization procedure is useful in a two-treatment situation where the sample sizes for the two study groups are to be equal or approximately equal. In this method, the researcher recruits participants in short blocks and ensures that half of participants in each block belong to either of the treatment group respectively (Corbett et al., 2015). For instance, the researcher can name the two groups A and B. Then, half of the participants in the sample block belong to treatment group A while the other half comprises study group B. However, it is essential to remember that the selection between the blocks is random.

There are research designs that do not meet all the requirements crucial for controlling the influence of external variables. That is, a researcher must make conclusions without randomizations. Researchers call these trials quasi-experiments. In such situations, researchers draw conclusions basing on logical deductions and plausibility.

To conclude, experiments play a significant role in the collection of crucial data about various variables. It is important that researchers minimize bias and threats to external validities to draw correct conclusions. Thus, researchers use randomization in psychology trials to assign participants to the treatment groups under study. Among the randomized methods available are simple randomization and block randomization. However, in cases where researchers must make conclusions without randomizations, they apply logic and plausibility. Nevertheless, randomization remains an essential aspect of maximizing efficacy and effectiveness in psychology experiments.